Building data visualization and exploration support into an embedded database system: Big Data in the small

关系数据库系统一直是，而且在许多方面继续是推动 数据革命。促成这场革命的这些系统的特点是相对高效、强大、 优雅的声明性查询语言和事务一致性。 Codd的数据独立性原则提供了一个抽象层，该抽象层允许应用程序和 用户在逻辑级别处理数据，而不需要知道数据库系统如何处理数据 在物质层面上。数据独立性允许数据库引擎确保事务一致性；这 消除了应用程序执行此操作的需要。这笔交易使得写入范围广泛的大数据 应用程序明显更轻松。 但当然，这种讨价还价也是有代价的。一种局限于“查询语言”的接口， 如SQL和XQuery，提供了。虽然SQL已经发展成为一个相当称职的关系数据平台 具有讽刺意味的是，对于支持业务应用程序的复杂分析查询，它的性能很差 适用于数据探索和可视化任务。同时，大数据的探索和可视化 如今，收集数据变得与过去的传统数据管理一样重要。 我们计划为各种类型的SQL查询提供无比例、渐进式、直接的聚合数据可视化 向支持数据探索迈进。

Relational database systems have been, and in many ways continue to be, the main driving engine behind the data revolution. The hallmarks of these systems that enabled the revolution are relative efficiency, powerful, elegant, declarative query languages, and transactional consistency. Codd's Principle of Data Independence provided for a layer of abstraction that has allowed applications and users to deal with the data at a logical level, without need to know how the database system deals with the data at the physical level. Data independence allows for the database engine to ensure transactional consistency; this removes the need for application programs to do so. This bargain has made writing a wide range of large data applications significantly easier. But this bargain does come with costs too, of course. One is limited to what the "query-language" interfaces, such as SQL and Xquery, provide. While SQL has evolved into a quite competent platform for relational data management and for complex analytic queries backing business applications, it is, with no small irony, poorly suited for data exploration and visualization tasks. Meanwhile, exploration and visualization of large data collections has become just as important a need today as has been traditional data management in the past. We plan to provide a scale-free, progressive, direct visualization of aggregate data for types of SQL queries towards supporting data exploration.